AI & Data Systems Associate in Leatherhead

Location: Based 3 days on-site at Sunswap’s Leatherhead HQ (KT22 7BA, UK)

Salary: up to £45,000 (dependent on experience)

About us:

Every refrigerated truck and trailer moving goods across the country has relied on diesel for decades. It's polluting, expensive, and the technology hasn't changed. Two companies have dominated the market, selling the same legacy technology with minor updates. Sunswap makes the electric refrigeration that's replacing it - zero emissions, lower operating costs, built from the ground up for how fleets actually operate. We've gone from founding to manufacturing at scale globally in just six years. We built our own production facility in Surrey. We are trusted by major brands like Tesco, Ocado, Birds Eye, and Happy Eggs every single day. With units operating across the UK, France, Netherlands, Belgium, Chile, and Australia. We've been named Sunday Times Best Place to Work 2024 and 2025. We've won awards for innovation, technology, and marketing excellence. We've secured backing from BGF, Barclays, and Clean Growth Fund. We've got the customers, the traction, and the momentum. Now we're taking this British-engineered product global. Scaling manufacturing. Opening new markets. Building the team that will make legacy polluting technology obsolete across the world.

If you want to be part of a company that's setting new standards for the industry and scaling internationally, this is it.

We are seeking an AI & Data Systems Associate to support the development of Sentinel, Sunswap’s AI maintenance, diagnostics and service orchestration platform. Sentinel is being built to transform how our electric transport refrigeration units are monitored, diagnosed and supported in the field. It brings together telemetry, alerts, service data, diagnostic logic, machine learning and AI workflows to help identify issues earlier, recommend the right next action and support faster, better service decisions. This is not a generic AI project. Sentinel is practical AI applied to real hardware, real customers and real operational problems. The goal is to move beyond manual dashboard monitoring and reactive service response towards a more intelligent, scalable and eventually closed-loop maintenance system for the global cold chain.

This role is ideal for a highly capable early-career generalist who is excited by AI, data, systems thinking and practical problem solving. You will work across case gathering, data structuring, backtesting, analysis, workflow development and system evaluation. You will help turn messy real-world information into structured evidence that can be used to test, improve and scale Sentinel. We are especially interested in someone who is AI-native. You should already be using AI tools regularly and intelligently to research, code, analyse, automate, write, debug and learn faster. You do not need years of commercial experience, but you should be sharp, curious, hard-working and willing to get stuck into both the interesting systems work and the detailed handle-turning needed to make the system reliable.

Key responsibilities:

• Support the development of Sentinel across AI workflows, data, diagnostics, evaluation and service operations.
• Gather and structure real-world cases from alerts, service incidents, engineering investigations, customer issues and fault histories.
• Help build datasets that can be used to test Sentinel’s diagnostic and recommendation logic.
• Run backtesting and evaluation exercises against historical cases.
• Analyse false positives, false negatives, edge cases, missed issues and ambiguous outputs.
• Help define and refine failure modes, indicators, diagnostic signals, confidence levels and escalation pathways.
• Review telemetry, alerts, event histories and service records to identify patterns, anomalies and evidence of component degradation.
• Use AI tools as a core part of your workflow, including for research, coding, data cleaning, synthesis, documentation, debugging and automation.
• Test AI-generated outputs carefully, challenge assumptions and ensure work is accurate, explainable and useful.
• Support the creation of repeatable evaluation workflows, rather than one-off analyses.
• Help document diagnostic workflows, datasets, test methods, assumptions and open questions.
• Work closely with engineering, service and operations colleagues to understand how faults are investigated and how Sentinel can improve that process.
• Support the development of SOPs to be used in collaboration with Sentinel to deliver in field repair & diagnostics.
• Support human-in-the-loop review by helping capture feedback and turn lessons learned into better rules, workflows and evaluations.
• Communicate findings clearly with both technical and non-technical colleagues.

Qualifications & experience:

• A degree or equivalent experience in a relevant subject such as AI, data science, computer science, maths, engineering, physics, economics or another analytical discipline.
• A strong interest in AI, data systems, connected hardware, automation and practical problem solving.
• Evidence that you are AI-native and already use AI tools regularly to improve how you learn, work, code, research, analyse or automate.
• Strong analytical ability, with the ability to break ambiguous problems into clear steps.
• High attention to detail and a willingness to do careful, repetitive work where needed.
• Strong written communication skills, with the ability to explain technical or analytical findings clearly.
• A proactive, curious and hard-working approach, with the ability to learn quickly and ask good questions.

Advantageous:

• Experience using AI tools to build personal projects, automations, agents, workflows or prototypes.
• Experience using Python for data analysis, automation, modelling, scripting or personal projects.
• Experience with pandas, Jupyter notebooks, SQL, KQL, APIs or lightweight web tools.
• Experience with data labelling, evaluation, backtesting, QA, case review or test dataset creation.
• Experience working with telemetry, time-series data, alerts, logs, fault codes or operational datasets.
• Exposure to N8N, Make, Azure, ADX, Microsoft Fabric, Logic Apps, Microsoft Foundry, Grafana, Airtable or similar systems.
• A master’s degree or PhD in a relevant analytical, technical or scientific field.
• Experience working in a startup, engineering, logistics, climate tech, manufacturing, automotive or another fast-paced technical environment.
• Evidence of independent learning, such as a GitHub repo, notebook, project, research paper, portfolio, technical write-up, AI workflow or personal automation.